def loadChains(self, root, files_or_samples, weights=None, loglikes=None,
ignore_lines=None):
"""
Loads chains from files.
:param root: Root name
:param files_or_samples: list of file names or list of arrays of samples, or single array of samples
:param weights: if loading from arrays of samples, corresponding list of arrays of weights
:param loglikes: if loading from arrays of samples, corresponding list of arrays of -2 log(likelihood)
:param ignore_lines: Amount of lines at the start of the file to ignore, None if should not ignore
:return: True if loaded successfully, False if none loaded
"""
self.chains = []
self.samples = None
self.weights = None
self.loglikes = None
if ignore_lines is None: ignore_lines = self.ignore_lines
WSkwargs = {"ignore_rows": ignore_lines,
"min_weight_ratio": self.min_weight_ratio}
if isinstance(files_or_samples, six.string_types) or isinstance(files_or_samples[0], six.string_types):
# From files
if weights is not None or loglikes is not None:
raise ValueError('weights and loglikes not needed reading from file')
if isinstance(files_or_samples, six.string_types): files_or_samples = [files_or_samples]
self.name_tag = self.name_tag or os.path.basename(root)
for fname in files_or_samples:
if print_load_details: print(fname)
self.chains.append(WeightedSamples(fname, **WSkwargs))
nchains = len(self.chains)
if not nchains:
raise WeightedSampleError('loadChains - no chains found for ' + root)
else:
# From arrays
dim = array_dimension(files_or_samples)
if dim in [1, 2]:
self.setSamples(slice_or_none(files_or_samples, ignore_lines),
slice_or_none(weights, ignore_lines),
slice_or_none(loglikes, ignore_lines), self.min_weight_ratio)
if self.paramNames is None:
self.paramNames = ParamNames(default=self.n)
nchains = 1
elif dim == 3:
for i, samples_i in enumerate(files_or_samples):
self.chains.append(WeightedSamples(
samples=samples_i, loglikes=None if loglikes is None else np.atleast_2d(loglikes)[i],
weights=None if weights is None else np.atleast_2d(weights)[i], **WSkwargs))
if self.paramNames is None:
self.paramNames = ParamNames(default=self.chains[0].n)
nchains = len(self.chains)
else:
raise ValueError('samples or files must be array of samples, or a list of arrays or files')
self._weightsChanged()
return nchains > 0
def __init__(self, means, covs, weights=None, lims=None, names=None, label='', labels=None):
"""
:param means: list of means for each Gaussian in the mixture
:param covs: list of covariances for the Gaussians in the mixture
:param weights: optional weight for each component (defaults to equal weight)
:param lims: optional list of hard limits for each parameter, [[x1min,x1max], [x2min,x2max]]; use None for no limit
:param names: list of names (strings) for each parameter. If not set, set to "param1", "param2"...
:param label: name for labelling this mixture
:param labels: list of latex labels for each parameter. If not set, defaults to p_{1}, p_{2}...
"""
self.means = np.asarray(means)
self.dim = self.means.shape[1]
self.covs = [np.array(cov) for cov in covs]
self.invcovs = [np.linalg.inv(cov) for cov in self.covs]
if weights is None: weights = [1. / len(means)] * len(means)
self.weights = np.array(weights, dtype=np.float64)
if np.sum(self.weights) <= 0:
raise ValueError('Weight <= 0 in MixtureND')
self.weights /= np.sum(weights)
self.norms = (2 * np.pi) ** (0.5 * self.dim) * np.array([np.sqrt(np.linalg.det(cov)) for cov in self.covs])
self.lims = lims
self.paramNames = ParamNames(names=names, default=self.dim, labels=labels)
self.names = self.paramNames.list()
self.label = label
self.total_mean = np.atleast_1d(np.dot(self.weights, self.means))
self.total_cov = np.zeros((self.dim, self.dim))
for mean, cov, weight, totmean in zip(self.means, self.covs, self.weights, self.total_mean):
self.total_cov += weight * (cov + np.outer(mean - totmean, mean - totmean))
def loadChains(self, root, files, ignore_lines=None):
"""
Loads chains from files.
:param root: Root name
:param files: list of file names
:param ignore_lines: Amount of lines at the start of the file to ignore, None if should not ignore
:return: True if loaded successfully, False if none loaded
"""
self.chains = []
self.samples = None
self.weights = None
self.loglikes = None
self.name_tag = self.name_tag or os.path.basename(root)
for fname in files:
if print_load_details: print(fname)
self.chains.append(
WeightedSamples(fname,
ignore_lines or self.ignore_lines,
min_weight_ratio=self.min_weight_ratio))
if len(self.chains) == 0:
raise WeightedSampleError('loadChains - no chains found for ' +
root)
if self.paramNames is None:
self.paramNames = ParamNames(default=self.chains[0].n)
self._weightsChanged()
return len(self.chains) > 0
def setParamNames(self, names=None):
"""
Sets the names of the params.
:param names: Either a :class:`~.paramnames.ParamNames` object, the name of a .paramnames file to load, a list of name strings,
otherwise use default names (param1, param2...).
"""
self.paramNames = None
if isinstance(names, ParamNames):
self.paramNames = names
elif isinstance(names, six.string_types):
self.paramNames = ParamNames(names)
elif names is not None:
self.paramNames = ParamNames(names=names)
elif self.samples is not None:
self.paramNames = ParamNames(default=self.n)
if self.paramNames:
self._getParamIndices()
